An intelligent port container data security storage method and system

Abstract

The present invention relates to the technical field of data processing, and particularly relates to an intelligent port container data security storage method and system, including: obtaining a plurality of sets of container data of several dimensions for two adjacent days; obtaining the data specification uniformity of each set of container data of each dimension; obtaining the updated specification uniformity of each set of container data of each dimension; screening all sets of container data of all dimensions through the updated specification uniformity to obtain a high-standard dimension container data set; obtaining the encoding priority of each high-standard dimension container data set according to the quantity difference of container data that meet the standard specifications between container data sequences in different time periods; performing data compression on all sets of container data of all dimensions according to the encoding priority to obtain all sets of compressed container data of all dimensions; storing all sets of compressed container data of all dimensions. The present invention improves the storage efficiency of port container data.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to an intelligent port container data secure storage method and system. Background Art

[0002] "Port container" refers to the process of loading goods into containers and conducting centralized management and scheduling at the port; this process is a key link in modern port logistics, involving multiple steps such as goods packing, stacking, handling, and ship loading; since there are many types of container data, including structured data and unstructured data, it is necessary to consider using suitable data compression methods when storing data to reduce storage space and improve transmission efficiency; when using Huffman coding to compress container data, due to the requirement that the packed containers need to have the same specifications, there is a high degree of repetition in container data. If the repetition of container data is not considered and only the unordered original container data is used as the input of the Huffman coding algorithm, the data compression efficiency will be greatly reduced, the storage space will increase, and the storage efficiency of port container data will decrease. Summary of the Invention

[0003] To solve the above problems, the present invention provides an intelligent port container data secure storage method and system.

[0004] An embodiment of the present invention provides an intelligent port container data secure storage method, which includes the following steps:

[0005] Obtain a plurality of sets of container data of several dimensions for two adjacent days; each set of container data of a dimension includes a sequence of container data for several time periods, and each sequence of container data for a time period includes several container data;

[0006] According to the difference situation of the container data in the sequence of container data for the same time period between each set of container data of each dimension for two adjacent days, obtain the data specification uniformity of each set of container data of each dimension; correct the data specification uniformity according to the difference of the container data between the sequences of container data for adjacent time periods, and obtain the updated specification uniformity of each set of container data of each dimension; screen all sets of container data of all dimensions through the updated specification uniformity to obtain a set of high-standard dimension container data;

[0007] According to the quantity difference of the container data that meet the standard specifications between the sequences of container data for different time periods, obtain the coding priority of each set of high-standard dimension container data;

[0008] Data compression is performed on all types of dimensional container data sets according to the coding priority to obtain all types of dimensional container data sets after compression; the compressed all types of dimensional container data sets are stored.

[0009] Preferably, the method for obtaining the data specification uniformity of each type of dimensional container data set according to the difference situation of the container data in the container data sequence in the same time period between each type of dimensional container data sets of adjacent two days includes the following specific methods:

[0010] For any type of dimensional container data set, according to the difference of the maximum value of the container data in the container data sequence of each time period between the container data sets of this type of dimension of adjacent two days, obtain the container data difference of each time period between the container data sets of this type of dimension of adjacent two days;

[0011] Obtain the difference value of the concentrated time period between the container data sets of this type of dimension of adjacent two days;

[0012] Record the cumulative sum of the container data differences of all time periods between the container data sets of this type of dimension of adjacent two days as the first cumulative sum; record the reciprocal of the difference value of the concentrated time period between the container data sets of this type of dimension of adjacent two days as the first reciprocal; take the product of the first reciprocal and the first cumulative sum as the data specification uniformity of this type of dimensional container data set.

[0013] Preferably, the method for obtaining the container data difference of each time period between the container data sets of this type of dimension of adjacent two days includes the following specific methods:

[0014] For any container data in the container data sequence of any time period in any type of dimensional container data set; record the absolute value of the difference between the container data and the mean value of all container data in the container data sequence of the time period as the deviation value of the container data; in the container data sequence of the time period, record the container data with the maximum deviation value as the target container data in the container data sequence of the time period; record the ratio of the target container data in the container data sequence of the time period on the first day to the target container data in the container data sequence of the time period on the second day as the first ratio; take the absolute value of the difference between 1 and the first ratio as the container data difference of the time period between the container data sets of the said type of dimension of adjacent two days.

[0015] Preferably, the method for obtaining the difference value of the concentrated time period between the container data sets of this type of dimension of adjacent two days includes the following specific methods:

[0016] For any set of container data of a certain dimension, in the container data sequences of all time periods in the set of container data of this dimension on the first day, record the serial number of the time period to which the largest target container data belongs as the first serial number; in the container data sequences of all time periods in the set of container data of this dimension on the second day, record the serial number of the time period to which the largest target container data belongs as the second serial number; take the absolute value of the difference between the first serial number and the second serial number as the data concentration time period difference value between the sets of container data of this dimension on two adjacent days.

[0017] Preferably, the method for correcting the data specification unity according to the difference in container data between the container data sequences of adjacent time periods and obtaining the updated specification unity of each set of container data of a certain dimension includes the following specific method:

[0018] According to the difference in container data between the container data sequences of adjacent time periods, obtain the specification unity correction factor of each set of container data of a certain dimension;

[0019] For any set of container data of a certain dimension, take the product of the inverse proportional value of the specification unity correction factor of the set of container data of this dimension and the data specification unity of the set of container data of this dimension as the updated specification unity of the set of container data of this dimension.

[0020] Preferably, the method for obtaining the specification unity correction factor of each set of container data of a certain dimension includes the following specific method:

[0021] For the container data sequence of any time period in any set of container data of a certain dimension, record the average value of all container data in the container data sequence of this time period as the first average value of the container data sequence of this time period; record the absolute value of the difference between the first average value of the container data sequence of this time period and the first average value of the container data sequence of the next time period as the first absolute difference value of the container data sequence of this time period; take the average value of the first absolute difference values of the container data sequences of all time periods in the set of container data of this dimension as the specification unity correction factor of the set of container data of this dimension.

[0022] Preferably, the method for screening all sets of container data of a certain dimension through the updated specification unity to obtain a set of high-standard dimension container data includes the following specific method:

[0023] Preset a threshold parameter , for any set of container data of a certain dimension, if the updated specification unity of the set of container data of this dimension is greater than or equal to the threshold parameter , denote the set of container data of the said dimension as the high-standard dimension container data set.

[0024] Preferably, the method for obtaining the coding priority of each high-standard dimension container data set according to the quantity difference of container data that meet the standard specifications between container data sequences in different time periods specifically includes:

[0025] For any container data sequence in any time period in any high-standard dimension container data set, denote the ratio of the quantity of container data that meet the standard specifications in the container data sequence of the said time period to the quantity of all container data in the container data sequence of the said time period as the second ratio of the container data sequence of the said time period; denote the average value of the quantity of container data that meet the standard specifications in the container data sequences of all time periods in the high-standard dimension container data set as the second average value; denote the absolute value of the difference between the quantity of container data that meet the standard specifications in the container data sequence of the said time period and the second average value as the absolute value of the second difference of the container data sequence of the said time period; denote the product of the second ratio of the container data sequence of the said time period and the absolute value of the second difference of the container data sequence of the said time period as the first product of the container data sequence of the said time period; denote the accumulated sum of the first products of the container data sequences of all time periods in the high-standard dimension container data set as the second accumulated sum; use the product of the updated specification uniformity of the high-standard dimension container data set and the second accumulated sum as the coding priority of the high-standard dimension container data set.

[0026] Preferably, the method for data compression of all sets of container data of all dimensions according to the coding priority to obtain the compressed sets of container data of all dimensions specifically includes:

[0027] Obtain the original coding length of each set of container data of each dimension according to the Huffman coding algorithm;

[0028] For any high-standard dimension container data set, denote the inverse ratio value of the coding priority of the high-standard dimension container data set as the first inverse ratio value; use the product of the first inverse ratio value and the original coding length of the high-standard dimension container data set as the updated coding length of the high-standard dimension container data set;

[0029] Input the updated coding lengths of all high-standard dimension container data sets into the Huffman coding algorithm to perform data compression on all sets of container data of all dimensions, and obtain the compressed sets of container data of all dimensions.

[0030] The present invention also provides an intelligent port container data security storage system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of any one of the intelligent port container data security storage methods are implemented.

[0031] The beneficial effects of the technical solution of the present invention are as follows: The data specification unity is corrected according to the differences in container data between the container data sequences in adjacent time periods, and the updated specification unity of each dimension container data set is obtained; all dimension container data sets are screened through the updated specification unity to obtain a high-standard dimension container data set; thus, a dimension container data set with high repeatability is obtained; according to the quantity difference of container data meeting the standard specification between the container data sequences in different time periods, the encoding priority of each high-standard dimension container data set is obtained; all dimension container data sets are compressed according to the encoding priority to obtain all compressed dimension container data sets; all compressed dimension container data sets are stored; different encoding lengths are assigned to the high-standard dimension container data sets according to the encoding priority, thereby improving the data compression efficiency and further improving the storage efficiency of port container data. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or in the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0033] Figure 1 It is a flowchart of the steps of an intelligent port container data security storage method of the present invention;

[0034] Figure 2 It is a characteristic relationship flowchart of an intelligent port container data security storage of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features, and effects of an intelligent port container data security storage method and system according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this invention belongs.

[0037] The following specifically describes the specific solutions of an intelligent port container data security storage method and system provided by the present invention in conjunction with the accompanying drawings.

[0038] Please refer to Figure 1 , which shows a flowchart of the steps of an intelligent port container data security storage method provided by an embodiment of the present invention. The method includes the following steps:

[0039] Step S001: Obtain a set of container data of several dimensions for two adjacent days.

[0040] Specifically, first, it is necessary to collect a set of container data of several dimensions for two adjacent days. The specific process is as follows:

[0041] For any day, every 10 minutes is used as a time period. Each time, the six types of container data, namely the size, weight, volume, packing time, storage location, and estimated departure time of all containers in the port, are collected in sequence for 24 hours; the sequence formed by all data under each type of container data dimension within each time period is used as the container data sequence of each dimension; the set formed by all data under each type of container data dimension within all time periods is used as the container data set of each dimension for that day.

[0042] Among them, each container data set of each dimension includes container data sequences of several time periods, and each container data sequence of each time period includes several container data.

[0043] Thus, a set of container data of several dimensions for two adjacent days is obtained through the above method.

[0044] Step S002: Obtain the data specification uniformity of each container data set of each dimension according to the difference of container data in the container data sequences of the same time period between each container data set of each dimension for two adjacent days; correct the data specification uniformity according to the difference of container data between adjacent time periods of container data sequences, and obtain the updated specification uniformity of each container data set of each dimension; screen all container data sets of all dimensions through the updated specification uniformity to obtain a high-standard dimension container data set.

[0045] It should be noted that container data has relatively regular data characteristics and their change forms. For example, due to the limitations of the container specifications, the sizes of containers are distributed only at several standard size values, so the data corresponding to such sizes has a high degree of repeatability. Also, the packing time, the corresponding arrival time and departure time, etc. will also have corresponding periodicity due to the influence of container data and sizes, that is, each dimension of container data set corresponds to a data specification unity. And the greater the periodicity of each dimension of container data set, the more standard the container data in that dimension of container data set. Correspondingly, the higher the obtained data specification unity. Since the smaller the difference between the container data in the container data sequences of adjacent time periods, and the higher the periodicity of the corresponding dimension of container data set, the data specification unity of each dimension of container data set can be corrected by the difference between the container data in the container data sequences of each time period, and the updated specification unity of each dimension of container data set can be obtained. By screening all dimensions of container data sets with the updated specification unity, a high-standard dimension container data set can be obtained.

[0046] 1. Obtain the data specification unity of each dimension of container data set.

[0047] It should be noted that by analyzing the time characteristics of each dimension of container data set, for some shipping routes, they may arrive and depart at a fixed time every week, then the corresponding packing activities will also show periodicity. At this time, dimensions such as packing time and packing size will reflect the concentration of data at a certain moment, and then its periodicity will be higher. The higher the standardness of the container data set, the more necessary it is to analyze the repeatability, and then the higher the priority given to the corresponding coding length. And the periodicity is an analysis carried out on the time series, that is, the different dimensions of container data sets are more concentrated in a certain time period under the time series, and the closer this time period is within two adjacent days in the time series. For example, the dimension of container data set on the first day is concentrated at 2 pm, and the dimension of container data set on the second day is also concentrated at 2 pm, then the data periodicity and repeatability of this dimension of container data set are higher, and the corresponding data dimension standardness is higher, that is, the data specification unity is greater.

[0048] Preferably, in some implementation manners of the embodiments of the present invention, the calculation method for obtaining the data specification unity of each dimension of container data set according to the difference situation of the container data in the container data sequences of the same time period between each dimension of container data sets of adjacent two days is as follows:

[0049] For any dimensional container data set, according to the difference in the maximum container data in the container data sequence for each time period between the container data sets of this dimension on two adjacent days, obtain the container data difference for each time period between the container data sets of this dimension on two adjacent days;

[0050] Obtain the difference value of the concentrated time period between the container data sets of this dimension on two adjacent days;

[0051] Denote the cumulative sum of the container data differences for all time periods between the container data sets of this dimension on two adjacent days as the first cumulative sum; denote the reciprocal of the difference value of the concentrated time period between the container data sets of this dimension on two adjacent days as the first reciprocal; take the product of the first reciprocal and the first cumulative sum as the data specification uniformity of the container data set of this dimension.

[0052] The specific formula is:

[0053]

[0054] In the formula, represents the data specification uniformity of the th dimensional container data set; represents the difference value of the concentrated time period of the th dimensional container data set on two adjacent days; represents the total number of container data sequences for all time periods in the dimensional container data set; represents the th dimensional container data set on two adjacent days, and the th container data difference for the time period; represents the exponential function with the natural constant as the base. In the embodiment, the model is used to present the inverse proportional relationship, is the input of the model, and the implementer can select the inverse proportional function according to the actual situation.

[0055] It should be noted that the greater the container data difference for the time period in the dimensional container data set, the greater the difference in the container data for the same time period between the dimensional container data sets on two adjacent days, and the worse the periodicity, then the smaller the data specification uniformity of the dimensional container data set; the greater the difference value of the concentrated time period in the dimensional container data set, the greater the difference in the time periods in the concentration, and the worse the periodicity, then the smaller the data specification uniformity of the dimensional container data set.

[0056] Preferably, in some implementation manners of the embodiment of the present invention, the method for obtaining the container data difference for each time period between the container data sets of each dimension on two adjacent days includes:

[0057] For any container data in the container data sequence of any time period in any dimensional container data set; the absolute value of the difference between the container data and the mean value of all container data in the container data sequence of the time period is denoted as the deviation value of the container data; in the container data sequence of the time period, the container data with the maximum deviation value is denoted as the target container data in the container data sequence of the time period; the ratio of the target container data in the container data sequence of the time period on the first day to the target container data in the container data sequence of the time period on the second day is denoted as the first ratio; the absolute value of the difference between 1 and the first ratio is used as the container data difference of the time period between the container data sets of the two adjacent days of the dimensional container data set.

[0058] The specific formula is as follows:

[0059]

[0060] In the formula, represents the container data difference of the th dimensional container data set between two adjacent days for the th time period; represents the target container data in the container data sequence of the th dimensional container data set on the first day for the th time period; represents the target container data in the container data sequence of the th dimensional container data set on the second day for the th time period; represents taking the absolute value.

[0061] It should be noted that since the sign of the difference needs to be considered, for container data less than the mean value, not only its centrality is not considered, but it also does not participate in the construction of the standard degree, because container data less than the mean value does not have effective data characteristics; the closer the target container data of two adjacent days is, the more similar the change characteristics of the container data in the dimensional container data sets of two adjacent days are, that is, the dimensional container data sets of two adjacent days are more concentrated in a certain time period in the time series, and the higher the data specification unity of the dimensional container data set is.

[0062] Preferably, in some implementation manners of the embodiments of the present invention, the method for obtaining the data concentration time period difference value between the container data sets of each dimension between two adjacent days includes:

[0063] For any dimensional container data set, in the container data sequences of all time periods in the dimensional container data set on the first day, record the time period serial number to which the largest target container data belongs as the first serial number; in the container data sequences of all time periods in the dimensional container data set on the second day, record the time period serial number to which the largest target container data belongs as the second serial number; take the absolute value of the difference between the first serial number and the second serial number as the data set time period difference value between the dimensional container data sets of adjacent two days.

[0064] It should be noted that the data set time period difference value represents the difference in the time period serial numbers with the highest data centrality between the dimensional container data sets of adjacent two days. The smaller the difference, the higher the data periodicity and repeatability of the dimensional container data set; for example, if the dimensional container data set on the first day is concentrated at 2 pm and the dimensional container data set on the second day is also concentrated at 2 pm, then the higher the data periodicity and repeatability of the dimensional container data set, the higher the corresponding data dimension standard degree, that is, the greater the data specification unity.

[0065] Thus, the data specification unity of each dimensional container data set is obtained.

[0066] 2. Obtain all high-standard dimensional container data sets.

[0067] It should be noted that since the smaller the average value of the container data differences between the container data sequences of adjacent time periods, the higher the periodicity of the corresponding dimensional container data set, the data specification unity of each dimensional container data set can be corrected by the container data differences between the container data sequences of different time periods, and the updated specification unity of each dimensional container data set is obtained; all dimensional container data sets are screened by the updated specification unity to obtain high-standard dimensional container data sets.

[0068] Preferably, in some implementation manners of the embodiments of the present invention, the calculation method for correcting the data specification unity according to the differences in container data between the container data sequences of adjacent time periods and obtaining the updated specification unity of each dimensional container data set is as follows:

[0069] Obtain the specification unity correction factor of each dimensional container data set according to the differences in container data between the container data sequences of adjacent time periods;

[0070] For any dimensional container data set, take the product of the inverse ratio value of the specification unity correction factor of the dimensional container data set and the data specification unity of the dimensional container data set as the updated specification unity of the dimensional container data set.

[0071] The specific formula is as follows:

[0072]

[0073] In the formula, represents the updated specification uniformity of the -th dimensional container data set; represents the data specification uniformity of the -th dimensional container data set; represents the specification uniformity correction factor of the -th dimensional container data set; represents the exponential function with the natural constant as the base. In the embodiment, the model is used to present the inverse proportional relationship, is the input of the model, and the implementer can select the inverse proportional function according to the actual situation.

[0074] Preferably, in some implementation manners of the embodiment of the present invention, the method for obtaining the specification uniformity correction factor of each dimensional container data set includes:

[0075] For the container data sequence of any time period in any dimensional container data set, the mean value of all container data in the container data sequence of the time period is denoted as the first mean value of the container data sequence of the time period; the absolute value of the difference between the first mean value of the container data sequence of the time period and the first mean value of the container data sequence of the next time period is denoted as the first absolute difference value of the container data sequence of the time period; the mean value of the first absolute difference values of the container data sequences of all time periods in the dimensional container data set is used as the specification uniformity correction factor of the dimensional container data set.

[0076] Preferably, in some implementation manners of the embodiment of the present invention, the specific method for obtaining the high-standard dimensional container data set is as follows:

[0077] Preset a threshold parameter , where in this embodiment, is taken as an example for description, and this embodiment does not make specific limitations, where is determined according to the specific implementation situation.

[0078] For any dimensional container data set, if the updated specification uniformity of the dimensional container data set is greater than or equal to the threshold parameter , the dimensional container data set is denoted as the high-standard dimensional container data set.

[0079] So far, all high-standard dimension container data sets are obtained through the above method.

[0080] Step S003: Obtain the coding priority of each high-standard dimension container data set according to the quantity difference of container data that meets the standard specifications between container data sequences in different time periods.

[0081] It should be noted that each high-standard dimension container data set corresponds to a standard specification. Within different high-standard dimension container data sets, there are different standard specifications, and the occurrence frequencies of container data that meet the standard specifications are also different; then, under different standard specifications, the occurrence frequencies of container data will also be different. Since more attention is paid to container data with a higher occurrence frequency because its corresponding data has a higher repeatability, the priority during Huffman coding is higher; moreover, the container data under high-frequency standard specifications are closer, that is, the number of cargo ships belonging to the container data cycle under this high-standard dimension is larger, and such cargo ships all have relatively similar destinations and sailing distances; generally, due to the existence of its data characteristics, such as the size and type of the container, the container data will be distributed only at several standard size values due to the limitation of the box specifications, so the corresponding data of such sizes has a higher repeatability. Therefore, different standard data specifications have different occurrence frequencies, and the specification with a larger frequency characteristic has a higher priority.

[0082] Preferably, in some implementation manners of the embodiment of the present invention, the calculation method for obtaining the coding priority of each high-standard dimension container data set according to the quantity difference of container data that meets the standard specifications between container data sequences in different time periods is as follows:

[0083] For any container data sequence in any time period of a high-standard dimension container data set, the ratio of the number of container data that meet the standard specifications in the container data sequence of the time period to the number of all container data in the container data sequence of the time period is denoted as the second ratio of the container data sequence of the time period; the mean value of the number of container data that meet the standard specifications in the container data sequences of all time periods in the high-standard dimension container data set is denoted as the second mean value; the absolute value of the difference between the number of container data that meet the standard specifications in the container data sequence of the time period and the second mean value is denoted as the second absolute difference value of the container data sequence of the time period; the product of the second ratio of the container data sequence of the time period and the second absolute difference value of the container data sequence of the time period is denoted as the first product of the container data sequence of the time period; the sum of the first products of the container data sequences of all time periods in the high-standard dimension container data set is denoted as the second sum; the product of the updated specification uniformity of the high-standard dimension container data set and the second sum is used as the coding priority of the high-standard dimension container data set.

[0084] The specific calculation method is as follows:

[0085]

[0086] In the formula, represents the coding priority of the th high-standard dimension container data set; represents the updated specification uniformity of the th high-standard dimension container data set; represents the total number of container data sequences of all time periods in the dimension container data set; represents the number of container data that meet the standard specifications in the container data sequence of the th time period in the th high-standard dimension container data set; represents the number of all container data in the container data sequence of the th time period in the th high-standard dimension container data set; represents the mean value of the number of container data that meet the standard specifications in the container data sequences of all time periods in the th high-standard dimension container data set; represents taking the absolute value.

[0087] It should be noted that since there is a direct proportional relationship between the quantity and priority of container data that meets the standard specifications, when the updated specification uniformity of the high-standard dimension container data set is considered, it indicates that the stronger the periodic change law of the container data, the higher the degree of regularity of the container data. At this time, the higher the regularity corresponding to the high-standard dimension container data set, the more it can prove that the change trend of the container data in the high-standard dimension container data set is based on the original regular data, and thus its repeatability will be higher, and the priority in Huffman coding compression will be higher. Raising the priority of this high-standard dimension container data set into Huffman coding can greatly improve the coding compression efficiency.

[0088] Thus far, the coding priorities of each high-standard dimension container data set are obtained through the above method.

[0089] Step S004: Perform data compression on all types of dimension container data sets according to the coding priorities to obtain all types of dimension container data sets after compression; store all types of dimension container data sets after compression.

[0090] It should be noted that according to the principle of the Huffman coding algorithm, it can be known that the coding priority is inversely proportional to the coding length, that is, the higher the priority, the higher the data repeatability, and the higher the corresponding data frequency, so the coding length is shorter.

[0091] Preferably, in some implementation manners of the embodiments of the present invention, the specific method for performing data compression on all types of dimension container data sets according to the coding priorities to obtain all types of dimension container data sets after compression is as follows:

[0092] Obtain the original coding length of each type of dimension container data set according to the Huffman coding algorithm;

[0093] For any high-standard dimension container data set, record the inverse ratio value of the coding priority of the high-standard dimension container data set as the first inverse ratio value; take the product of the first inverse ratio value and the original coding length of the high-standard dimension container data set as the updated coding length of the high-standard dimension container data set;

[0094] Input the updated coding lengths of all high-standard dimension container data sets into the Huffman coding algorithm to perform data compression on all types of dimension container data sets, and obtain all types of dimension container data sets after compression.

[0095] It should be noted that for non-high-standard dimension container data sets, the original coding length is used for data compression; the Huffman coding algorithm is a prior art, and no further elaboration is made here in this embodiment.

[0096] Specifically, the compressed container data sets of all dimensions are stored in a database.

[0097] It should be noted that the compressed container data sets of all dimensions ensure the full utilization of the sensitivity of Huffman coding to repeated data, increase the efficiency in the data compression process, reduce the data compression ratio, improve the storage efficiency of container data sets in different dimensions, reduce the storage space, and play an efficient storage role.

[0098] Please refer to Figure 2 , which shows a characteristic relationship flowchart of intelligent port container data secure storage.

[0099] Through the above steps, an intelligent port container data secure storage is completed.

[0100] The present invention also proposes an intelligent port container data secure storage system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the intelligent port container data secure storage method described in steps S001 to S004 are implemented.

[0101] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent port container data security storage method, characterized in that, The method includes the following steps: Obtain a number of sets of container data for several dimensions for two adjacent days; each set of container data for a dimension includes a container data sequence for several time periods, and each container data sequence for a time period includes a number of container data; According to the difference situation of the container data in the container data sequences of the same time period between each set of container data for each dimension for two adjacent days, obtain the data specification uniformity of each set of container data for each dimension; according to the difference of the container data between the container data sequences of adjacent time periods, correct the data specification uniformity, and obtain the updated specification uniformity of each set of container data for each dimension; screen all sets of container data for all dimensions through the updated specification uniformity to obtain a set of high-standard dimension container data; According to the quantity difference of the container data that meet the standard specification between the container data sequences of different time periods, obtain the coding priority of each set of high-standard dimension container data; Perform data compression on all sets of container data for all dimensions according to the coding priority to obtain all sets of compressed container data for all dimensions; store all sets of compressed container data for all dimensions.

2. The intelligent port container data security storage method according to claim 1, wherein, The specific method for obtaining the data specification uniformity of each set of container data for each dimension according to the difference situation of the container data in the container data sequences of the same time period between each set of container data for each dimension for two adjacent days includes: For any set of container data for a dimension, according to the difference of the maximum container data in the container data sequences of each time period between this set of container data for this dimension for two adjacent days, obtain the container data difference of each time period between this set of container data for this dimension for two adjacent days; Obtain the difference value of the concentrated time period between this set of container data for this dimension for two adjacent days; Denote the cumulative sum of the container data differences of all time periods between this set of container data for this dimension for two adjacent days as the first cumulative sum; denote the reciprocal of the difference value of the concentrated time period between this set of container data for this dimension for two adjacent days as the first reciprocal; take the product of the first reciprocal and the first cumulative sum as the data specification uniformity of this set of container data for this dimension.

3. The intelligent port container data security storage method according to claim 2, wherein The specific method for obtaining the container data difference of each time period between this set of container data for this dimension for two adjacent days includes: For any container data in the container data sequence of any time period in any set of container data for a dimension; denote the absolute value of the difference between the container data and the mean value of all container data in the container data sequence of the time period as the deviation value of the container data; in the container data sequence of the time period, denote the container data with the maximum deviation value as the target container data in the container data sequence of the time period; Denote the ratio of the target container data in the container data sequence of the time period on the first day to the target container data in the container data sequence of the time period on the second day as the first ratio; Take the absolute value of the difference between 1 and the first ratio as the container data difference in the time period between the container data sets of the said dimension on two adjacent days.

4. The intelligent port container data security storage method according to claim 2, characterized in that, The specific method for obtaining the difference value of the concentrated time period between the container data sets of this dimension on two adjacent days includes: For any container data set of a dimension, in the container data sequences of all time periods in the container data set of this dimension on the first day, record the time period serial number to which the largest target container data belongs as the first serial number; in the container data sequences of all time periods in the container data set of this dimension on the second day, record the time period serial number to which the largest target container data belongs as the second serial number; take the absolute value of the difference between the first serial number and the second serial number as the difference value of the concentrated time period between the container data sets of this dimension on two adjacent days.

5. The method for securely storing intelligent port container data according to claim 1, characterized in that, The specific method for correcting the data specification unity according to the difference in container data between the container data sequences of adjacent time periods and obtaining the updated specification unity of each container data set of a dimension includes: Obtain the specification unity correction factor of each container data set of a dimension according to the difference in container data between the container data sequences of adjacent time periods. For any container data set of a dimension, take the product of the inverse ratio value of the specification unity correction factor of the container data set of this dimension and the data specification unity of the container data set of this dimension as the updated specification unity of the container data set of this dimension.

6. The intelligent port container data security storage method according to claim 5, characterized in that The specific method for obtaining the specification unity correction factor of each container data set of a dimension includes: For the container data sequence of any time period in any container data set of a dimension, record the mean value of all container data in the container data sequence of this time period as the first mean value of the container data sequence of this time period. Take the absolute value of the difference between the first mean value of the container data sequence of this time period and the first mean value of the container data sequence of the next time period as the first absolute difference value of the container data sequence of this time period. Take the mean value of the first absolute difference values of the container data sequences of all time periods in the container data set of this dimension as the specification unity correction factor of the container data set of this dimension.

7. The method for securely storing intelligent port container data according to claim 1, wherein The specific method for screening all container data sets of a dimension through the updated specification unity to obtain a high-standard dimension container data set includes: Preset a threshold parameter , for any kind of dimensional container data set, if the updated specification uniformity of the kind of dimensional container data set is greater than or equal to the threshold parameter , record the kind of dimensional container data set as a high-standard dimensional container data set.

8. The intelligent port container data security storage method according to claim 1, characterized in that The specific method for obtaining the coding priority of each high-standard dimension container data set according to the quantity difference of container data meeting the standard specification between the container data sequences of different time periods includes: For the container data sequence of any time period in any high-standard dimension container data set, record the ratio of the quantity of container data meeting the standard specification in the container data sequence of this time period to the quantity of all container data in the container data sequence of this time period as the second ratio of the container data sequence of this time period. Denote the mean value of the number of container data that meet the standard specifications in the container data sequences of all time periods in the high-standard dimension container data set as the second mean value; denote the absolute value of the difference between the number of container data that meet the standard specifications in the container data sequence of the time period and the second mean value as the second absolute difference value of the container data sequence of the time period; denote the product of the second ratio of the container data sequence of the time period and the second absolute difference value of the container data sequence of the time period as the first product of the container data sequence of the time period. Denote the cumulative sum of the first products of the container data sequences of all time periods in the high-standard dimension container data set as the second cumulative sum; take the product of the updated specification uniformity of the high-standard dimension container data set and the second cumulative sum as the coding priority of the high-standard dimension container data set.

9. The intelligent port container data security storage method according to claim 1, characterized in that The specific method for performing data compression on all types of dimension container data sets according to the coding priority to obtain the compressed all types of dimension container data sets includes: Obtain the original coding length of each type of dimension container data set according to the Huffman coding algorithm. For any high-standard dimension container data set, denote the inverse proportional value of the coding priority of the high-standard dimension container data set as the first inverse proportional value; take the product of the first inverse proportional value and the original coding length of the high-standard dimension container data set as the updated coding length of the high-standard dimension container data set. Input the updated coding lengths of all types of high-standard dimension container data sets into the Huffman coding algorithm to perform data compression on all types of dimension container data sets, and obtain the compressed all types of dimension container data sets.

10. An intelligent port container data security storage system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for securely storing intelligent port container data according to any one of claims 1-9.

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